INNER CODE UNIT · Python

model_inference

RichardS0268/CNN-for-Trading · inference.py:10

def model_inference(model, setting):
    
    model.eval()
    # iterate over test data
    sub_points = [setting.TEST.START_DATE] + [int(setting.TEST.END_DATE//1e4 * 1e4) + i*100 + 1 for i in range(4, 13, 3)] + [setting.TEST.END_DATE]

    symbol_factors = pd.DataFrame([], index=['code', 'date', 'up_factor']).T
    for m_idx in range(len(sub_points)-1):
        print(f"Inferencing: {sub_points[m_idx]} - {sub_points[m_idx+1]}")
        
        inference_dataset = _D.ImageDataSet(win_size = setting.DATASET.LOOKBACK_WIN, \
                                        start_date = sub_points[m_idx], \
                                        end_date = sub_points[m_idx+1], \
                                        mode = 'inference', \
                                        label = setting.TRAIN.LABEL, \
                                        indicators = setting.DATASET.INDICATORS, \
                                        show_volume = setting.DATASET.SHOW_VOLUME, \
                                        parallel_num=setting.DATASET.PARALLEL_NUM)

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